Curriculum Learning for Qubit Mapping Across Hardware Topologies

Abstract

Compiling quantum circuits for physical hardware requires an initial mapping step that assigns virtual qubits to physical qubits such that interacting pairs are placed on connected hardware locations. Current approaches train a separate agent per device topology, requiring significant compute for each new hardware generation and transferring no knowledge across devices. This work investigates whether curriculum learning --- progressively training a reinforcement learning agent on hardware topologies of increasing size --- can produce a single agent that generalises to unseen topologies. We evaluate three curriculum variants differing in replay ratio and warmup length, alongside three non-curriculum baselines, in the QGym InitialMapping environment using MaskablePPO. Results show that curriculum agents outperform single-topology and single-size training on held-out topologies, reaching strong frontier performance with greater sample efficiency than direct training. Against unordered exposure to the same topology distribution, however, curriculum ordering's advantage holds on the target topology size but not on generalisation to unseen topologies. While absolute performance remains modest and variance across seeds is substantial, the findings establish curriculum learning as a viable approach to topology-general qubit mapping and provide a proof of concept for training a single model that transfers across hardware topologies, reducing the computational cost of re-training for each new device.